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Pulse-shape Discrimination of Fast Neutron Background using Convolutional Neural Network for NEOS II

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Abstract

Pulse-shape discrimination plays a key role in improving the signal-to-background ratio in NEOS analysis by removing fast neutrons. Identifying particles by looking at the tail of the waveform has been an effective and plausible approach for pulse-shape discrimination, but has the limitation in sorting low energy particles. As a good alternative, the convolutional neural network can scan the entire waveform as they are to recognize the characteristics of the pulse and perform shape classification of NEOS data. This network provides a powerful identification tool for all energy ranges and helps to search unprecedented phenomena of low-energy, a few MeV or less, neutrinos.

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Acknowledgments

Y. Jeong and K. Siyeon thank C. Ha for helpful advice and discussion. This research was supported by the National Research Foundation Grant of Korea (NRF-2017R1A2B4004308), IBS-R016-D1, and the Chung-Ang University Graduate Research Scholarship in 2019.

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Authors and Affiliations

  1. Department of Physics, Chung-Ang University, Seoul, 06974, Korea

    Y. Jeong & K. Siyeon

  2. Neutron Science Division, Korea Atomic Energy Research Institute, Daejeon, 34057, Korea

    B. Y. Han, H. M. Lee & G. M. Sun

  3. Center for Underground Physics, Institute for Basic Science (IBS), Daejeon, 34126, Korea

    E. J. Jeon, Y. D. Kim, Y. J. Ko, M. H. Lee, J. Lee, Y. M. Oh, K. S. Park & S. H. Seo

  4. Department of Physics, Kyungpook National University, Daegu, 41566, Korea

    H. S. Jo, D. K. Kim & C. S. Moon

  5. Department of Physics and Astronomy, Sejong University, Seoul, 05006, Korea

    J. Y. Kim & Y. D. Kim

  6. Department of Physics, SungKyunKwan University, Suwon, 16419, Korea

    J. G. Kim & I. Yu

  7. IBS School, University of Science and Technology (UST), Daejeon, 34113, Korea

    Y. D. Kim & M. H. Lee

  8. Department of Accelerator Science, Korea University, Sejong, 30019, Korea

    H. K. Park

  9. Center for Ionizing Radiation, Korea Research Institute of Standards and Science, Daejeon, 34113, Korea

    Y. S. Yoon

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  1. Y. Jeong
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  2. B. Y. Han
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  3. E. J. Jeon
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  4. H. S. Jo
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  5. D. K. Kim
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  6. J. Y. Kim
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  7. J. G. Kim
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  8. Y. D. Kim
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  9. Y. J. Ko
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  10. H. M. Lee
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  12. J. Lee
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  13. C. S. Moon
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  14. Y. M. Oh
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  15. H. K. Park
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  16. K. S. Park
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  17. S. H. Seo
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  18. K. Siyeon
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  19. G. M. Sun
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  21. I. Yu
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The NEOS II Collaboration

Corresponding author

Correspondence to K. Siyeon.

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Jeong, Y., Han, B.Y., Jeon, E.J. et al. Pulse-shape Discrimination of Fast Neutron Background using Convolutional Neural Network for NEOS II. J. Korean Phys. Soc. 77, 1118–1124 (2020). https://doi.org/10.3938/jkps.77.1118

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  • DOI: https://doi.org/10.3938/jkps.77.1118

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